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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 2: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 4: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 5: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
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NEW QUESTION # 150
You have a Microsoft Foundry agent that grounds responses from an Azure Search index that contains the following:
- Searchable text fields for product names and product codes
- A vector field that stores embeddings for product descriptions
You need to ensure that users can query the index by using the following:
- Exact product names or codes
- Natural language descriptions of the products
What should you configure?
Answer: D
Explanation:
To meet your requirements, you need to configure a Hybrid Search with Semantic Ranking in Azure AI Search. This setup combines keyword matching for exact identifiers with vector search for natural language queries, delivering the most accurate grounding data to your Microsoft Foundry agent.
Reference:
https://www.tredence.com/blog/searchsmart-enhancing-rag-with-azure-ai-search-service
NEW QUESTION # 151
You are building an app that will use Azure AI to monitor workspaces for safety. You need to recommend a service that meets the following requirements:
* Generates alerts when employees enter high-risk areas
* Monitors video feeds in real time
* Minimizes development effort
What should you recommend?
Answer: A
Explanation:
Azure Vision in Foundry Tools Spatial Analysis is designed to process real-time streaming video and analyze the presence, movement, and spatial relationships of people in physical environments. It provides predefined operations such as personcrossingpolygon, which can identify when a person enters or exits a configured zone. The resulting personZoneEnterExitEvent can be consumed by an application to trigger alerts when an employee enters an area designated as high risk.
Spatial Analysis minimizes development effort because it supplies pretrained people-detection and zone- monitoring capabilities. Developers configure camera streams, polygonal zones, and event parameters instead of collecting images, labeling objects, and training a custom model.
Image Analysis primarily evaluates individual images and does not maintain movement or zone state across a live stream. Azure AI Video Indexer extracts searchable insights from video or live-stream content, but it is not the specialized option for detecting physical zone-entry events. Custom Vision object detection would require training data, labeling, model training, deployment, and additional application logic to track movement.
The Study Guide explicitly associates Spatial Analysis with detecting the presence and movement of people in video under Implement computer vision solutions # Analyze videos .
NEW QUESTION # 152
Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent accepts user-uploaded screenshots and uses a multimodal chat model.
Some screenshots contain potentially malicious embedded text.
You need to prevent a prompt injection attack and ensure that third-party content is treated as lower trust.
How should you configure prompt shields for document attacks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 153
You need to configure the model deployment for Agent1 to meet the technical requirements.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Deployment type: Standard
Version update policy: Once the current version expires
The correct deployment type is Standard . The case study specifies that Project1 is deployed in an EU Azure region and that model-processed data must remain within the EU. It also requires scalable, high-throughput generative AI workloads that dynamically handle variable customer support traffic without reserved throughput capacity. In Microsoft Foundry Models, Standard is a pay-per-token deployment type that processes data in a single Azure region, while Global Standard can process requests across regions and Global Provisioned uses reserved provisioned throughput. Microsoft's deployment-type guidance identifies Standard as single-region, pay-per-token, whereas Global Provisioned is cross-region with reserved capacity.
The correct version update policy is Once the current version expires . This keeps Agent1 on the selected model version during its supported lifecycle, which supports stable and consistent responses, but still preserves continuity by automatically moving to a supported replacement when the current version is retired.
Microsoft's model versioning guidance states that this policy updates only when the current model version expires, while upgrading when a new default is available changes the deployment sooner and opting out can cause the deployment to stop working after retirement. Reference topics: deployment types, regional data processing, model versioning, throughput capacity, and stable production deployments.
NEW QUESTION # 154
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You add a reflection pass that regenerates the response if the required clauses are missing.
Does this meet the goal?
Answer: B
Explanation:
Correct:
* You add a reflection pass that regenerates the response if the required clauses are missing.
This is Self-Correction Strategy: A reflection pass allows an agent to evaluate its own initial output against specified constraints (e.g., checking for the presence of mandatory regulatory clauses). If the required text is missing, the agent triggers a programmatic self-correction or regeneration loop to include them before final delivery.
Incorrect:
* You increase the value of the max_tokens parameter.
Increasing the max_tokens parameter prevents the response from being cut off mid-sentence due to length constraints. However, it does not force the model's logic to explicitly include missing information that it chose to leave out earlier in the text.
* You increase the value of the temperature parameter.
Raising the temperature parameter increases randomness and creativity. For rigid compliance tasks like summarizing regulatory documents, higher temperature actually increases the risk of hallucination and omission.
* You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
Evaluation Flow Block: Running an evaluation flow to score completeness and blocking bad responses identifies and stops low-quality outputs, but it does not fix or actively improve the response completeness. It simply filters failures out of the system.
Reference:
https://pub.towardsai.net/reflection-with-llm-how-to-make-ai-review-its-own-work-2db122fca1d8
NEW QUESTION # 155
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